Computational Methods for the Analysis and Prediction of EGFR-mutated Lung Cancer Drug Resistance : Recent Advances in Drug Design, Challenges and Future Prospects

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

5 Scopus Citations
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Author(s)

  • Tanvir Alam
  • Jia Wu
  • Victor H.F. Lee

Related Research Unit(s)

Detail(s)

Original languageEnglish
Number of pages19
Journal / PublicationIEEE/ACM Transactions on Computational Biology and Bioinformatics
Online published10 Jan 2022
Publication statusOnline published - 10 Jan 2022

Abstract

Lung cancer is a major cause of cancer deaths worldwide, and has a very low survival rate. Non-small cell lung cancer (NSCLC) is the largest subset of lung cancers, which accounts for about 85% of all cases. It has been well established that mutation in epidermal growth factor receptor (EGFR) can lead to lung cancer. EGFR Tyrosine Kinase Inhibitors are developed to target the kinase domain of EGFR. These TKIs produce promising results at initial stage of therapy, but the efficacy becomes limited due to the development of drug resistance. In this paper, we provide a comprehensive overview of computational methods, for understanding drug resistance mechanisms. Next, we evaluate the role of important EGFR parameters in drug resistance mechanism, including structural dynamics, stability, dimerization, binding free energies, and signaling pathways. Personalized drug resistance prediction models, drug response curves, drug synergy, and other data-driven methods are also discussed. We explore limitations in the current methodologies and discuss strategies to overcome them. We believe this review will serve as a reference for researchers; to apply computational techniques for precision medicine, analyzing structures of protein-drug complexes, drug discovery, and understanding the drug response and resistance mechanisms in lung cancer patients.

Research Area(s)

  • AlphaFold2, Biological system modeling, Computational methods, Computational modeling, Deep Learning, Drugs, Epidermal growth factor receptor (EGFR), Immune system, Inhibitors, Lung cancer, Molecular dynamics (MD) simulation, Molecular modeling, Non-small cell lung cancer (NSCLC), Proteins

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